It is well established that higher maternal circulating glucose results in bigger and fatter babies. The developmental over nutrition hypothesis extends this relationship and suggests that greater maternal fatness, pregnancy fat gain, gestational diabetes and higher maternal circulating glucose and other fuels such as fatty acids, program offspring to life-long greater fatness and adverse metabolic profiles. However, the mechanisms driving this relationship are still unknown. The overarching aim of the proposed work is to determine the role of metabolomics and epigenomic mechanisms in this relationship. This will be achieved using data from two unique and complementary UK birth cohorts: (i) The Avon Longitudinal Study of Parents and Children (ALSPAC) and Born in Bradford (BiB), each with N~ 13,000 index pregnancies. Both cohorts have/will have detailed and complementary data on maternal developmental over nutrition risk factors in pregnancy, maternal metabolomics assessed by NMR spectroscopy on pregnancy samples, fetal metabolomics assessed using cord blood serum, as well as on serum from several time points during infancy and adolescence. Offspring genome wide methylation patterns have been determined in cord blood white cells and again in childhood and adolescence. Measures of adiposity and metabolic health have been assessed repeatedly from birth to early adulthood. Moreover, ALSPAC offspring are now in their mid-20s and are having children of their own, enabling the investigation of the relationship between maternal risk factors for developmental over nutrition and offspring adiposity and metabolic health across two generations. The work proposed here will identify targets for interventions aimed at reducing the adverse impacts of developmental over nutrition on future generations. Given that 20-50% of women who start pregnancy are overweight or obese and that 15% of pregnancies are affected by GDM, this is extremely important. This work will also develop a framework for the appropriate analyses of large repeatedly assessed multi-'omic' data in birth cohorts.